Zhong-Ping JIANG received the B.Sc. degree in mathematics from the University of Wuhan, Wuhan, China, in 1988, the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, adaptive dynamic programming and their applications to information, mechanical and biological systems. He is coauthor of four books Stability and Stabilization of Nonlinear Systems (with Dr. I. Karafyllis, Springer, 2011), Nonlinear Control of Dynamic Networks (with Drs. T. Liu and D.J. Hill, Taylor & Francis, 2014), Robust Adaptive Dynamic Programming (with Y. Jiang, Wiley-IEEE Press, 2017) and Nonlinear Control Under Information Constraints (with T. Liu, Science Press, 2018). He also is the (co)author of 15 book chapters, 196 published/accepted journal papers, and numerous conference papers. His work has received over 18,200 citations with an h-index of 68, by Google Scholar.
Dr. Jiang is a Deputy co-Editor-in-Chief of the Journal of Control and Decision, a Senior Editor for the IEEE Control Systems Letters, an Editor for the International Journal of Robust and Nonlinear Control and has served as an Associate Editor for several journals including Mathematics of Control, Signals and Systems (MCSS), Systems & Control Letters, IEEE Transactions on Automatic Control, European Journal of Control, and Science China: Information Sciences. Dr. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council (1998), the CAREER Award from the U.S. National Science Foundation (2001), JSPS Invitation Fellowship from the Japan Society for the Promotion of Science (2005), the Distinguished Overseas Chinese Scholar Award from the NSF of China (2007), and the Chair Professorship by the Ministry of Education of China (2009). His recent awards include the Steve and Rosalind Hsia Best Biomedical Paper Award at the 2016 World Congress on Intelligent Control and Automation in Guilin, China, and the Best Paper Award at the 2017 Asian Control Conference, Gold Coast, Australia.
Prof. Jiang is a Fellow of the IEEE and a Fellow of the IFAC.
In this talk, a data-driven non-model-based approach is proposed for adaptive optimal control design for completely unknown continuous-time linear and nonlinear dynamical systems, driven by emerging applications from connected autonomous and human-operated vehicles. Integrating techniques from reinforcement learning, dynamic programming and modern nonlinear control, a systematic theory known under “robust adaptive dynamic programming (RADP)” is proposed.
A new class of learning-based adaptive optimal controllers is obtained without relying on the knowledge of system dynamics. Extensions to global nonlinear/adaptive optimal control and adaptive optimal tracking with disturbance rejection are studied.
In the second part of the talk, we show how this data-driven non-model-based control theory can be applied to solve the adaptive optimal control problem for connected autonomous and human-operated vehicles. For simplicity, we consider the scenarios where n human-driven vehicles only transmit motional data and an autonomous vehicle in the tail receives the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. The effectiveness of the proposed approaches is demonstrated via online learning control of connected vehicles in the Paramics’ traffic micro-simulation.